Technology is more powerful and more accessible than ever. But it’s still a means to an end, not an end in itself. What really matters is what it empowers people to do.
Marketing and Insights teams want to be more efficient by doing things right, and more effective by doing the right things, preferably at the same time. For an order that tall, additional support is always welcome, especially in times when teams are facing leaner budgets and fewer resources. Agentic AI has been seen as a beacon in the night, and indeed it can be, but only when it empowers people to do what they really need to do more of, better, and faster.
Gartner recently observed that more than 40% of agentic AI projects are expected to be canceled by the end of 2027, citing high costs, unclear value, and immature technology as major barriers. That finding echoes a point I made a few years ago, in a piece exploring what I then called the paradox of insights tech:
“Insights tech is essential, but it’s not the point.”
The industry has evolved in remarkable ways since I wrote that, but the principle still holds—no matter how many buckets of ‘Agent Washing’ get poured over it.
The risk of many AI initiatives isn’t that the AI won’t work; it’s that teams will waste precious time trying to retrofit AI into processes where it doesn’t belong, rather than focusing on where it adds clear value.
At the same time, Gartner’s report points to a significant opportunity: agentic systems are expected to drive 15% of work decisions by 2028. For teams under pressure to make smarter decisions faster, that’s not just a signal; it’s a challenge to get sharper about where and how AI can truly serve them.
But the question remains, how do you strike the balance to ensure that routine workflows and tasks are handled by automation, while ensuring that strategic tasks driving business impact remain human-led and supported by agentic AI?
Co-create Solutions to Cut Through the Noise
Well, I think it requires deep collaboration between technology suppliers and customers. This is something that we’ve prioritized since day one at Stravito, and it’s more important than ever to ensure that we’re developing technology in a way that enables our customers to achieve their goals. And instead of bending your workflows to fit a generic AI tool, it’s about designing AI to fit your world. When AI is tailored to your context, embedded in your existing ways of working, and tuned to your team’s language and logic, it becomes a multiplier, not a blocker.
Ever since we’ve doubled down on our AI capabilities, we’ve seen our customers achieve incredible things, and they push us to be better and to innovate in ways that make an impact.
For example, the insights team at HEINEKEN has been at the forefront of piloting meaningful AI use cases in their platform, with impressive results. Their GenAI initiatives last year already saw 71% more of the content in their platform being used, and an estimated savings of €900,000 worth of CMI time that is now dedicated to more actionable tasks and strategic thinking.
Another great example comes from our customer Newell Brands. Melanie Huet, Newell Brands’ Co-CEO of Home and Commercial, recently commented on the impact that they’ve seen from implementing Stravito’s AI platform:
We were able to unlock all of our insights and research globally. We have thousands of users... We’ve accelerated our innovation pipeline, and it’s been one of the easiest and biggest unlocks of data I’ve had across the company where we can truly hinge results back to what we’ve done.”
— Melanie Huet, Co-CEO of Home and Commercial, Newell Brands
This reflects a broader shift we’re seeing: when organizations pair the right agentic architecture with clear, high-impact workflows, they unlock real enterprise value, fast.
Forecasting the Next Phase
From our conversations with insights leaders, and what we’re seeing across the industry, it’s clear that the next evolution in AI for insights isn’t just about faster outputs and complete automation. It’s about smarter, more strategic collaboration between people and technology that lead to better outcomes.
We expect to see growing momentum around three powerful shifts:
- Human-AI co-creation
Thanks to deeper integrations, AI is becoming more than a productivity booster; it’s turning into a true thinking partner. Instead of simply automating tasks, connecting AI to trusted data sources can help teams with framing problems, and exploring potential solutions, strategies, and new market directions.
The only way to build an assistant for the innovation process is through an iterative process. Human feedback can help to find a balance between which parts of the process require automation or a faster starting point for productivity.
- Synthetic data
The promise of synthetic data is quickly becoming a reality, with AI unlocking the potential to quickly deliver scalable and trusted consumer-insight rooted in research for real-time decision making. In fact, recent research published in Harvard Business Review showed that 81% of survey respondents “already use or plan to use GenAI to create synthetic data.”
From simulating market responses to building dynamic, always-current consumer personas grounded in real-time data. Synthetic data, when applied effectively, allows teams to stress test ideas early, prioritize where to invest and decide whether further concept ideation is worth the spend.
This marks a major shift, particularly in processes such as innovation, and solves a persistent challenge: too often ideas and concepts are built on outdated or subjective consumer insight. Synthetic data combats that by delivering trusted, up-to-date feedback instantly. The result? Less waste, reduced risk, and better ideas brought to market, faster.
- Customizable agents
Alongside general purpose Assistants there will be a focus on bespoke intelligent assistants that deeply understand your organization’s unique context. These agents are not just helpful, they are trained to speak your brand's language, understand nuanced business objectives, comply with your governance and data privacy rules, and are customized to the specific needs of different brands, categories and regions and markets across complex organizations.
Looking Ahead
We're in the middle of a fundamental redefinition of what insights work means. Now, more than ever, insights teams must become the central nervous system that helps organizations navigate complexity and uncertainty in real time.
It's no longer just about reporting what happened; it's about enabling faster, smarter decisions that drive strategic business outcomes.
And instead of bending your workflows to fit a generic AI tool, it’s about designing AI to fit your world. When AI is tailored to your context and tuned to your team’s language and logic, it becomes a multiplier, not a blocker.
This means we’re going to see a lot of incredible innovation, both when it comes to how GenAI evolves but also in what teams are able to do with it.
One of the best parts of my job is getting to build the future of insights with the people who actually use them. And while I can’t reveal specifics just yet, I can say this:
Some of the most exciting developments we’ve talked about here may be closer than you think.
If this piece resonated with you, be sure to look for my recent conversation on the Insighter’s Club Podcast, where I dig deeper into what I’ve learned this year and where I see the industry going.
